IOCCG Ocean Optics and Biogeochemistry Protocols for Satellite Ocean Colour Sensor Validation Volume 7.0. Aquatic Primary Productivity Field Protocols for Satellite Validation and Model Synthesis. (IOCCG Protocols Series, Volume 7.0). DOI: http://dx.doi.org/10.25607/OBP-1835

In 2018, a working group sponsored by the NASA Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) project, in conjunction with the International Ocean Colour Coordinating Group (IOCCG), European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), and Japan Aerospace Exploration Agency (JAXA), was assembled with the aim to develop community consensus on multiple methods for measuring aquatic primary productivity used for satellite validation and model synthesis. A workshop to commence the working group efforts was held December 5–7, 2018, at the University Space Research Association headquarters in Columbia, MD, USA, bringing together 26 active researchers from 16 institutions. In this document, we discuss and develop the workshop findings as they pertain to primary productivity measurements, including the essential issues, nuances, definitions, scales, uncertainties, and ultimately best practices for data collection across multiple methodologies

Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion

Activity coefficients, which are a measure of the nonideality of liquid mixtures, are a key property in chemical engineering with relevance to modeling chemical and phase equilibria as well as transport processes. Although experimental data on thousands of binary mixtures are available, prediction methods are needed to calculate the activity coefficients in many relevant mixtures that have not been explored to date. In this report, we propose a probabilistic matrix factorization model for predicting the activity coefficients in arbitrary binary mixtures. Although no physical descriptors for the considered components were used, our method outperforms the state-of-the-art method that has been refined over three decades while requiring much less training effort. This opens perspectives to novel methods for predicting physicochemical properties of binary mixtures with the potential to revolutionize modeling and simulation in chemical engineering

Comparison and evaluation of retrospective intermodality brain image registration techniques

International audiencePURPOSE: The primary objective of this study is to perform a blinded evaluation of a group of retrospective image registration techniques using as a gold standard a prospective, marker-based registration method. To ensure blindedness, all retrospective registrations were performed by participants who had no knowledge of the gold standard results until after their results had been submitted. A secondary goal of the project is to evaluate the importance of correcting geometrical distortion in MR images by comparing the retrospective registration error in the rectified images, i.e., those that have had the distortion correction applied, with that of the same images before rectification. METHOD: Image volumes of three modalities (CT, MR, and PET) were obtained from patients undergoing neurosurgery at Vanderbilt University Medical Center on whom bone-implanted fiducial markers were mounted. These volumes had all traces of the markers removed and were provided via the Internet to project collaborators outside Vanderbilt, who then performed retrospective registrations on the volumes, calculating transformations from CT to MR and/ or from PET to MR. These investigators communicated their transformations again via the Internet to Vanderbilt, where the accuracy of each registration was evaluated. In this evaluation, the accuracy is measured at multiple volumes of interest (VOIs), i.e., areas in the brain that would commonly be areas of neurological interest. A VOI is defined in the MR image and its centroid c is determined. Then, the prospective registration is used to obtain the corresponding point c' in CT or PET. To this point, the retrospective registration is then applied, producing c" in MR. Statistics are gathered on the target registration error (TRE), which is the distance between the original point c and its corresponding point c". RESULTS: This article presents statistics on the TRE calculated for each registration technique in this study and provides a brief description of each technique and an estimate of both preparation and execution time needed to perform the registration. CONCLUSION: Our results indicate that retrospective techniques have the potential to produce satisfactory results much of the time, but that visual inspection is necessary to guard against large errors